Workflow and Visualization for Localization of Concentric Pipe Collars

ABSTRACT

A method and system for visualizing data to detect a collar. A method may comprise disposing an electromagnetic logging tool downhole; emitting an electromagnetic field from the transmitter; energizing a casing with the electromagnetic field to produce an eddy current; recording the eddy current from the casing with the receiver; creating a variable-density-log from the recorded eddy current; selecting a wrapping period for the variable-density-log; creating a wrapped-variable-density-log from the variable-density-log using the wrapping period; and determining at least one collar location and a pipe index with the wrapped-variable-density-log. A system for to detect a collar may comprise an electromagnetic logging tool. The electromagnetic logging tool may comprise a transmitter and a receiver, wherein the transmitter and the receiver may be a coil. The system may further comprise an information handling system.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.16/334,671, filed Mar. 19, 2019, which is a national stage entry ofPCT/US2018/042895, filed Jul. 19, 2018, which claims the benefits ofU.S. Provisional Patent Application No. 62/542,385, filed Aug. 8, 2017,which are incorporated by reference herein in their entirety.

BACKGROUND

For oil and gas exploration and production, a network of wells,installations and other conduits may be established by connectingsections of metal pipe together. For example, a well installation may becompleted, in part, by lowering multiple sections of metal pipe (i.e., acasing string) into a wellbore, and cementing the casing string inplace. In some well installations, multiple casing strings are employed(e.g., a concentric multi-string arrangement) to allow for differentoperations related to well completion, production, or enhanced oilrecovery (EOR) options.

Corrosion of metal pipes is an ongoing issue. Efforts to mitigatecorrosion include use of corrosion-resistant alloys, coatings,treatments, and corrosion transfer, among others. Also, efforts toimprove corrosion monitoring are ongoing. For downhole casing strings,various types of corrosion monitoring tools are available. One type ofcorrosion monitoring tool uses electromagnetic (EM) fields to estimatepipe thickness or other corrosion indicators. As an example, an EMlogging tool may collect data on pipe thickness to produce an EM log.The EM log data may be interpreted to determine the condition ofproduction and inter mediate casing strings, tubing, collars, filters,packers, and perforations. When multiple casing strings are employedtogether, correctly managing corrosion detection EM logging tooloperations and data interpretation may be complex.

BRIEF DESCRIPTION OF THE DRAWINGS

These drawings illustrate certain aspects of some examples of thepresent disclosure, and should not be used to limit or define thedisclosure.

FIG. 1 illustrates an example of an EM logging tool disposed in awellbore;

FIG. 2 illustrates an example of a visualization for locating a collar;

FIG. 3 illustrates an example of a Wrapped Image Visualization display;

FIG. 4A illustrates an example of Wrapped Image Visualization beforecorrecting an incorrect collar identification;

FIG. 4B illustrated an example of Wrapped Image Visualization aftercorrecting an incorrect collar identification;

FIG. 5A illustrates another example of a tuned Wrapped ImageVisualization;

FIG. 5B illustrates another example of a tuned Wrapped ImageVisualization without removal of a collar signal;

FIG. 5C illustrates another example of a tuned Wrapped ImageVisualization with removal of a collar signal;

FIG. 6A illustrates a diamond pattern;

FIG. 6B illustrates a chevron pattern;

FIG. 6C illustrates a double-peak corrected pattern; and

FIG. 7 illustrates an example flow chart of an automated inversionworkflow for determining the location of a collar with a collar locatoralgorithm;

FIG. 8 illustrates an underlying algorithm that may be utilized forinversion software to locate a collar;

FIG. 9 illustrates an examples of a flow chart for an inversionalgorithm that may be utilized to locate a collar;

FIG. 10 illustrates an example flow chart for determining the locationof a collar; and

FIG. 11 illustrates another inversion example for determine the locationof a collar.

DETAILED DESCRIPTION

This disclosure may generally relate to methods for identifying collarswith electromagnetic logging tool. Electromagnetic (EM) sensing mayprovide continuous in situ measurements of parameters related to theintegrity of pipes in cased boreholes. As a result, EM sensing may beused in cased borehole monitoring applications. EM logging tools may beconfigured for multiple concentric pipes (e.g., for one or more) withthe first pipe diameter varying (e.g., from about two inches to aboutseven inches or more). EM logging tools may measure eddy currents todetermine metal loss and use magnetic cores at the transmitters. The EMlogging tools may use pulse eddy current (time-domain) and may employmultiple (long, short, and transversal) coils to evaluate multiple typesof defects in double pipes. It should be noted that the techniquesutilized in time-domain may be utilized in frequency-domainmeasurements. The EM logging tools may operate on a conveyance. EMlogging tool may include an independent power supply and may store theacquired data on memory. A magnetic core may be used in defect detectionin multiple concentric pipes.

In EM logging tools, the interpretation of the data may be based ondifferences between responses at two different points within the EM log,a point representing a nominal section and a point where thickness maybe estimated. The response differences may be processed to determine thechange in wall thickness within a tubular.

FIG. 1 illustrates an operating environment for an EM logging tool 100as disclosed herein. EM logging tool 100 may comprise a transmitter 102and/or a receiver 104. In examples, EM logging tool 100 may be aninduction tool that may operate with continuous wave execution of atleast one frequency. This may be performed with any number oftransmitters 102 and/or any number of receivers 104, which may bedisponed on EM logging tool 100. In additional examples, transmitter 102may function and/or operate as a receiver 104. EM logging tool 100 maybe operatively coupled to a conveyance 106 (e.g., wireline, slickline,coiled tubing, pipe, downhole tractor, and/or the like) which mayprovide mechanical suspension, as well as electrical connectivity, forEM logging tool 100. Conveyance 106 and EM logging tool 100 may extendwithin casing string 108 to a desired depth within the wellbore 110.Conveyance 106, which may include one or more electrical conductors, mayexit wellhead 112, may pass around pulley 114, may engage odometer 116,and may be reeled onto winch 118, which may be employed to raise andlower the tool assembly in the wellbore 110. Signals recorded by EMlogging tool 100 may be stored on memory and then processed by displayand storage unit 120 after recovery of EM logging tool 100 from wellbore110. Alternatively, signals recorded by EM logging tool 100 may beconducted to display and storage unit 120 by way of conveyance 106.Display and storage unit 120 may process the signals, and theinformation contained therein may be displayed for an operator toobserve and stored for future processing and reference. Alternatively,signals may be processed downhole prior to receipt by display andstorage unit 120 or both downhole and at surface 122, for example, bydisplay and storage unit 120. Display and storage unit 120 may alsocontain an apparatus for supplying control signals and power to EMlogging tool 100. Typical casing string 108 may extend from wellhead 112at or above ground level to a selected depth within a wellbore 110.Casing string 108 may comprise a plurality of joints 130 or segments ofcasing string 108, each joint 130 being connected to the adjacentsegments by a collar 132. There may be any number of layers in casingstring 108. For example, a first casing 134 and a second casing 136. Itshould be noted that there may be any number of casing layers.

FIG. 1 also illustrates a typical pipe string 138, which may bepositioned inside of casing string 108 extending part of the distancedown wellbore 110. Pipe string 138 may be production tubing, tubingstring, casing string, or other pipe disposed within casing string 108.Pipe string 138 may comprise concentric pipes. It should be noted thatconcentric pipes may be connected by collars 132. EM logging tool 100may be dimensioned so that it may be lowered into the wellbore 110through pipe string 138, thus avoiding the difficulty and expenseassociated with pulling pipe string 138 out of wellbore 110.

In logging systems, such as, for example, logging systems utilizing theEM logging tool 100, a digital telemetry system may be employed, whereinan electrical circuit may be used to both supply power to EM loggingtool 100 and to transfer data between display and storage unit 120 andEM logging tool 100. A DC voltage may be provided to EM logging tool 100by a power supply located above ground level, and data may be coupled tothe DC power conductor by a baseband current pulse system.Alternatively, EM logging tool 100 may be powered by batteries locatedwithin the downhole tool assembly, and/or the data provided by EMlogging tool 100 may be stored within the downhole tool assembly, ratherthan transmitted to the surface during logging (corrosion detection).

EM logging tool 100 may be used for excitation of transmitter 102.Transmitter 102 may transmit electromagnetic fields into subterraneanformation 142. The electromagnetic fields from transmitter 102 may bereferred to as a primary electromagnetic field. It should be noted thattransmitter 102 may be a coil, solenoid, or permanent magnet. Theprimary electromagnetic fields may produce Eddy currents in casingstring 108 and pipe string 138. These Eddy currents, in turn, producesecondary electromagnetic fields that may be sensed along with theprimary electromagnetic fields by receivers 104. Characterization ofcasing string 108 and pipe string 138, including determination of pipeattributes, may be performed by measuring and processing theseelectromagnetic fields. Pipe attributes may include, but are not limitedto, pipe thickness, pipe conductivity, and/or pipe permeability.

As illustrated, receivers 104 may be positioned on the EM logging tool100 at selected distances (e.g., axial spacing) away from transmitters102. It should be noted that receiver 102 may be a coil, solenoid,magnetometer, or Hall effect sensors. The axial spacing of receivers 104from transmitters 102 may vary, for example, from about 0 inches (0 cm)to about 40 inches (101.6 cm) or about two inches (5.08 cm) to aboutfour hundred inches (1016 cm). It should be understood that theconfiguration of EM logging tool 100 shown on FIG. 1 is merelyillustrative and other configurations of EM logging tool 100 may be usedwith the present techniques. A spacing of 0 inches (0 cm) may beachieved by collocating coils with different diameters. While FIG. 1shows only a single array of receivers 104, there may be multiple sensorarrays where the distance between transmitter 102 and receivers 104 ineach of the sensor arrays may vary. In addition, EM logging tool 100 mayinclude more than one transmitter 102 and more or less than six of thereceivers 104. In addition, transmitter 102 may be a coil implementedfor transmission of magnetic field while also measuring EM fields, insome instances. Where multiple transmitters 102 are used, theiroperation may be multiplexed or time multiplexed. For example, a singletransmitter 102 may transmit, for example, a multi-frequency signal or abroadband signal. While not shown, EM logging tool 100 may include atransmitter 102 and receiver 104 that are in the form of coils orsolenoids coaxially positioned within a downhole tubular (e.g., casingstring 108) and separated along the tool axis. Alternatively, EM loggingtool 100 may include a transmitter 102 and receiver 104 that are in theform of coils or solenoids coaxially positioned within a downholetubular (e.g., casing string 108) and collocated along the tool axis.

Transmission of EM fields by the transmitter 102 and the recordation ofsignals by receivers 104 may be controlled by display and storage unit120, which may include an information handling system 144. Asillustrated, the information handling system 144 may be a component ofthe display and storage unit 120. Alternatively, the informationhandling system 144 may be a component of EM logging tool 100. Aninformation handling system 144 may include any instrumentality oraggregate of instrumentalities operable to compute, estimate, classify,process, transmit, receive, retrieve, originate, switch, store, display,manifest, detect, record, reproduce, handle, or utilize any form ofinformation, intelligence, or data for business, scientific, control, orother purposes. For example, an information handling system 144 may be apersonal computer, a network storage device, or any other suitabledevice and may vary in size, shape, performance, functionality, andprice. Information handling system 144 may include a processing unit 146(e.g., microprocessor, central processing unit, etc.) that may processEM log data by executing software or instructions obtained from anon-transitory computer readable media 148 (e.g., optical disks,magnetic disks) that is local. The non-transitory computer readablemedia 148 may store software or instructions of the methods describedherein. Non-transitory computer readable media 148 may include anyinstrumentality or aggregation of instrumentalities that may retain dataand/or instructions for a period of time. Non-transitory computerreadable media 148 may include, for example, storage media such as adirect access storage device (e.g., a hard disk drive or floppy diskdrive), a sequential access storage device (e.g., a tape disk drive),compact disk, CD-ROM, DVD, RAM, ROM, electrically erasable programmableread-only memory (EEPROM), and/or flash memory; as well ascommunications media such wires, optical fibers, microwaves, radiowaves, and other electromagnetic and/or optical carriers; and/or anycombination of the foregoing. Information handling system 144 may alsoinclude input device(s) 150 (e.g., keyboard, mouse, touchpad, etc.) andoutput device(s) 152 (e.g., monitor, printer, etc.). The input device(s)150 and output device(s) 152 provide a user interface that enables anoperator to interact with EM logging tool 100 and/or software executedby processing unit 146. For example, information handling system 144 mayenable an operator to select analysis options, view collected log data,view analysis results, and/or perform other tasks.

EM logging tool 100 may use any suitable EM technique based on Eddycurrent (“EC”) for inspection of concentric pipes (e.g., casing string108 and pipe string 138). EC techniques may be particularly suited forcharacterization of a multi-string arrangement in which concentric pipesare used. EC techniques may include, but are not limited to,frequency-domain EC techniques and time-domain EC techniques.

In frequency domain EC techniques, transmitter 102 of EM logging tool100 may be fed by a continuous sinusoidal signal, producing primarymagnetic fields that illuminate the concentric pipes (e.g., casingstring 108 and pipe string 138). The primary electromagnetic fieldsproduce Eddy currents in the concentric pipes. These Eddy currents, inturn, produce secondary electromagnetic fields that may be sensed alongwith the primary electromagnetic fields by the receivers 104.Characterization of the concentric pipes may be performed by measuringand processing these electromagnetic fields.

In time domain EC techniques, which may also be referred to as pulsed EC(“PEC”), transmitter 102 may be fed by a pulse. Transient primaryelectromagnetic fields may be produced due the transition of the pulsefrom “off” to “on” state or from “on” to “off” state (more common).These transient electromagnetic fields produce EC in the concentricpipes (e.g., casing string 108 and pipe string 138). The EC, in turn,produce secondary electromagnetic fields that may be measured byreceivers 104 placed at some distance on the EM logging tool 100 fromtransmitter 102, as shown on FIG. 1 . Alternatively, the secondaryelectromagnetic fields may be measured by a co-located receiver (notshown) or with transmitter 102 itself.

It should be understood that while casing string 108 is illustrated as asingle casing string, there may be multiple layers of concentric pipesdisposed in the section of wellbore 110 with casing string 108. EM logdata may be obtained in two or more sections of wellbore 110 withmultiple layers of concentric pipes. For example, EM logging tool 100may make a first measurement of pipe string 138 comprising any suitablenumber of joints 130 connected by collars 132. Measurements may be takenin the time-domain and/or frequency range. EM logging tool 100 may makea second measurement in a casing string 108 of first casing 134, whereinfirst casing 134 comprises any suitable number of pipes connected bycollars 132. Measurements may be taken in the time-domain and/orfrequency domain. These measurements may be repeated any number of timesand for second casing 136 and/or any additional layers of casing string108. In this disclosure, as discussed further below, methods may beutilized to determine the location of any number of collars 132 incasing string 108 and/or pipe string 138. Determining the location ofcollars 132 in the frequency domain and/or time domain may allow foraccurate processing of recorded data in determining properties of casingstring 108 and/or pipe string 138 such as corrosion. As mentioned above,measurements may be taken in the frequency domain and/or the timedomain.

In frequency domain EC, the frequency of the excitation may be adjustedso that multiple reflections in the wall of the pipe (e.g., casingstring 108 or pipe string 138) are insignificant and the spacing betweentransmitters 102 and/or receiver 104 is large enough that thecontribution to the mutual impedance from the dominant (but evanescent)waveguide mode is small compared to the contribution to the mutualimpedance from the branch cut component, the remote-field eddy current(RFEC) effect may be observed. In a RFEC regime, the mutual impedancebetween the coil of transmitter 102 and coil of one of the receivers 104may be sensitive to the thickness of the pipe wall. To be more specific,the phase of the impedance varies as:

$\begin{matrix}{\varphi = {2\sqrt{\frac{\omega\mu\sigma}{2}}t}} & (1)\end{matrix}$

and the magnitude of the impedance shows the dependence:

$\begin{matrix}{\exp\left\lbrack {{- 2}\left( \sqrt{\frac{\omega\mu\sigma}{2}} \right)t} \right\rbrack} & (2)\end{matrix}$

where ω is the angular frequency of the excitation source, μ is themagnetic permeability of the pipe, σ is the electrical conductivity ofthe pipe, and t is the thickness of the pipe. By using the commondefinition of skin depth for the metals as:

$\begin{matrix}{\delta = \sqrt{\frac{2}{\omega\mu\sigma}}} & (3)\end{matrix}$

The phase of the impedance varies as:

$\begin{matrix}{\varphi \cong {2\frac{t}{\delta}}} & (4)\end{matrix}$

and the magnitude of the impedance shows the dependence:

$\begin{matrix}{\exp\left\lbrack \frac{{- 2}t}{\delta} \right\rbrack} & (5)\end{matrix}$

In RFEC, the estimated quantity may be the overall thickness of themetal. Thus, for multiple concentric pipes, the estimated parameter maybe the overall or sum of the thicknesses of the pipes. The quasi-linearvariation of the phase of mutual impedance with the overall metalthickness may be employed to perform fast estimation to estimate theoverall thickness of multiple concentric pipes. For this purpose, forany given set of pipes dimensions, material properties, and toolconfiguration, such linear variation may be constructed quickly and maybe used to estimate the overall thickness of concentric pipes.Information handling system 144 may enable an operator to selectanalysis options, view collected log data, view analysis results, and/orperform other tasks.

Monitoring the condition of pipe string 138 and casing string 108 may beperformed on information handling system 144 in oil and gas fieldoperations. Information handling system 144 may be utilized withElectromagnetic (EM) Eddy Current (EC) techniques to inspect pipe string138 and casing string 108. EM EC techniques may include frequency-domainEC techniques and time-domain EC techniques. In time-domain andfrequency-domain techniques, one or more transmitters 102 may be excitedwith an excitation signal and receiver 104 may record the reflectedexcitation signal for interpretation. The received signal isproportional to the amount of metal that is around transmitter 102 andreceiver 104. For example, less signal magnitude is typically anindication of more metal, and more signal magnitude is an indication ofless metal. This relationship may be utilized to determine metal loss,which may be due to an abnormality related to the pipe such corrosion orbuckling.

Referring to FIG. 1 , collars 132 may be another feature that mayinfluence measurements of metal loss or gain. Collars 132 are mechanicalpieces that connect segments of pipe string 138 and/or casing string 108together. This may allow pipe string 138 and/or casing string 108 to bebroken into segments (typically between 25-45 feet (7.62-13.716 meters)long) which may allow operators to store and/or transport them usingreadily available transportation methods. Collars 132 typically appearin the interpretation results as metal gain, since the overlappingthreads associated with collars 132 result in an overall gain in thethickness of the pipe, in which a collar 132 may be disposed.Identifying collars 132 in inspection may be beneficial to an operator.For example, collars 132 may be factored into corrosion interpretationyielding better corrosion estimates. Additionally, where collars 132 maybe disposed may be reported to customers as a final product. Collars 132may be utilized as a quality check on metal loss inversion performanceand where collars 132 may be disposed may help make future productionoptimization decisions and help identify position of various featuressuch as chokes, vanes, and/or other pipe jewelry.

In examples in which a pipe string 138 and a single casing string 108are utilized, identification of collars 132 may be simple. However, asthe number of pipes within casing string 108 may be increased, itbecomes difficult to identify collars 132 in recorded data. For example,it may become difficult to associate the features in the data to acorrect pipe number and take the effect of collar 132 out for improvedinversion to determine metal loss. Even though automated collar locationalgorithms have been proposed before, those algorithms may only providean initial estimate for “easy” collar positions and cannot provide areliable solution in the presence of large number of concentric pipes.On the other hand, each collar 132 that is misinterpreted by automaticcollar location algorithms results in substantial amount of delay andlabor time for manual correction. Information handling system 144 may beutilized to improve the workflow and visualization of data to minimizesuch inefficiencies in the collar locator process.

FIG. 2 illustrates current visualization 200 of collars 132 disposed onpipe string 138 and/or casing string 108 (Referring to FIG. 1 ), whichmay include a well plan 202, data in curve format 204, data in VDL(variable-density-log) format 206, and collar locations marked eitherwith special markers or as blips in a curve (not illustrated).Identification of collars 132 may include, selecting a proper channel(combination of frequency and receiver) that accentuates a desiredcollar 132 (typically a shallow reading receiver, which may read betweenabout four inches (10.16 cm) to about twenty inches (50.8 cm) from EMlogging tool 100, and high(er) frequency, which may be above 2 Hz for afirst pipe collar and for outer pipe collars a frequency of about 0.1Hz), inspecting the “collar-like” features manually, checking for priorpicks of collars 132 or “collar-like” features above and below to makesure that expected periodicity is observed, marking the center of collar132 at the channel that was chosen, shift processing zone by anapproximate collar length (30-40 feet (9.144-12.192 meters)) below andrepeat. Typically the process starts from a collar 132 that may beidentifiable and transitions to collars 132 that may not beidentifiable. Inspecting pipes with collars 132 that may not beidentifiable, identification of collars 132 that may be identifiablehelps eliminate ambiguities and incorrect identification. Thisinformation may be used in a collar locating algorithm and/or inversion,discussed below, to determine the location of collars 132. Results fromthe collar location algorithm or inversion may be visualized byinformation handling system 144 for an operator.

FIG. 3 illustrates Wrapped Image Visualization 300. In this case, theVDL is broken into sections of a certain length L (L=31.05 (9.464meters) feet in the figure) and each segment of pipe is drawnside-by-side as a different column 302 of the image. For example, theleft-most column is the VDL from approximately 0 feet to 31.05 feet, andthe next column is the VDL from 31.05 feet to 62.10 feet (9.46-18.928meters), and so on. What this immediately accomplishes is that the VDLof the whole well may be shown in a single image, rather than userscrolling a typical log (such as that in FIG. 1 ) up and down. WrappedImage Visualization 300 illustrates that any pipe feature, such ascollar 132 (Referring to FIG. 1 ), with a periodicity approximatelyequal to L shows up as a horizontally continuous pattern in a wrappedimage with periodicity L. For example, FIG. 2 shows that a L of 31.05feet (9.464 meters) produces a horizontal pattern 304 (cutting acrossright in the middle) which indicates that a pipe with each collar 132approximately 31.05 feet (9.464 meters) from each other and location ofeach collar 132 in that pipe may be immediately identified using avisualization with L substantially approximately equal to 31.05 feet(9.464 meters).

FIGS. 4A and 4B illustrates Wrapped Image Visualization 300, when“tuned” to a particular pipe periodicity, may allow an operator toresolve ambiguities associated with determining where collar 132 may bedisposed on pipe string 138 and casing string 108 (Referring to FIG. 1). For example, based on the expected smooth behavior of the horizontalpattern 400 (white dotted line), determination of incorrectidentification, such as third identification 402 as illustrated in FIG.4A (each identification 404 is represented as a white dot) may be morefeasible compared to using a standard non-wrapped visualization of theVDL such as that in FIG. 2 . After third identification 402 iscorrected, horizontal pattern 400 appears smooth as shown in FIG. 4B,verifying that the set of identifications may be feasible.

FIG. 5A shows an example with a plurality of pipes, where proper collaridentification 500 may be difficult without Wrapped Image Visualization300 (Referring to FIG. 300 ). Wrapped Image Visualization 300 may bedisplay an image for a first pipe, identify collars 132, and then moveto a second pipe, identify collar 132, and follow this processincrementally until collars 132 in the image and in the well plan may beidentified. In particular, an operator may be given a slider knob andinteractively adjust wrap periodicity L until a horizontal pattern isobtained. The wrapped image may be updated frequently enough forefficient interactive “tuning” by the operator. Such tuning may also beperformed by an algorithm with a matched filter.

In examples, recorded data may be scanned to identify repetitions andreport the periodicity of such repetitions to the operator to allow forquicker tuning. Such information may be presented as a curve thatindicates the strength of repetition at each periodicity and potentiallyoverlay it on the slider knob.

Identifying the locations of collars 132 on at least one pipe may allowfor the calculation of an ideal signature and subtract such idealsignature from the existing identified collars 132 to obtain a clearerimage of where collars 132 may be disposed on pipe string 138 and/orcasing string 108. For example, given N_(i) picks for pipe i that arecharacterized by the depth d_(j) ^(i) of the pick, where j is the pickindex, the ideal signature can be calculated as

$\begin{matrix}{{S^{i}\left( {d,c} \right)} = \left\{ \begin{matrix}{{{\underset{j}{MEDIAN}\left( {{VD}{L^{i}\left( {{d - d_{j}^{i}}\ ,c} \right)}} \right)}\ L_{\min}} < L < L_{\max}} \\{0\ {otherwise}}\end{matrix} \right.} & (6)\end{matrix}$

where L_(min) and L_(max) bounds that determine the size of thesignature which may be chosen as L_(min)=−10 feet (3.048 meters),L_(max)=10 feet (3.048 meters), MEDIAN is the median function appliedindividually for each combination of depth and channel index c, VDL isthe i'th VDL image values in Volts, Amps, Impedance, or normalizedunits. In case number of picks, N_(i), is smaller than 3, a simple meanmay be substituted for the median function.

An updated VDL may be constructed by subtracting the calculated idealsignature from the previous VDL as:

$\begin{matrix}{{VD{L^{i + 1}\left( {d,c} \right)}} = {{VD{L^{i}\left( {d,c} \right)}} - {\sum\limits_{j}{S^{i}\left( {{d - d_{j}^{i}},c} \right)}}}} & (7)\end{matrix}$

FIG. 5B shows Wrapped Image Visualization 300 before removal of collarsignal and FIG. 5C illustrates Wrapped Image Visualization 300 aftercollar signature removal may be applied. Comparing FIGS. 5B and 5C,removing collar signatures may allow for resolving ambiguities inidentification of collars 132 and increase the efficiency of theoperator for collar identification 500 for deeper pipes.

Wrapped Image Visualization 300 (Referring to FIG. 3 ) may utilizeddifferent pattern such as the “diamond” pattern 600, as illustrated inFIG. 6A, that may be composed by ordering the channels in a particularway in a VDL construction. Diamond pattern 600 makes it more intuitivefor operators to identify collars 132 and identify positions foroverlapping collars 132 (Referring to FIG. 1 ). Diamond patterns 600 maybe formed by ordering the channels in the following way:

D1AR1F1 ... D1AR1FM ...... D1ARNF1 ... D1ARNFM D1PRNFM ... D1PRNF1...... D1PR1FM ... D1PR1F1 ...... ...... ...... ...... ...... ............ ...... DKAR1F1 ... DKAR1FM ...... DKARNF1 ... DKARNFM DKPRNFM ...DKPRNF1 ...... DKPR1FM ... DKPR1F1

Here DX indicates depth X, A indicates amplitude, P indicates phase, RXindicates receiver X, and FX indicates frequency X. A total of Nreceivers, M frequencies and K depths are assumed in the above VDLdefinition. As shown above, diamond pattern 600 may be constructed byordering receivers 104 (Referring to FIG. 1 ) from shallow to deep foramplitude and continuing on this pattern by switching to phase withreceiver 104 for deep amplitude and coming back down to receivers 104(Referring to FIG. 1 ) for shallow amplitude. Ordering receivers 104 mayrefer to the distance between transmitters 102 and receivers 104. Thecloser transmitter 102 and receiver 104 are the shallower the amplitudemay be. As the distance between transmitter 102 and receiver 104 isexpanded the deeper the amplitude becomes. It is noted that certainsimilar alternate ordering may also be considered. For example, sameamplitude data may be used for the right half, rather than using phase.Similarly, phase may be used for the left half of diamond pattern 600rather than amplitude.

An alternative VDL pattern is chevron pattern 602 as described below:

D1AR1F1 D1PR1F1 ... D1AR1FM D1PR1FM ...... D1ARNF1 D1PRNF1 ... D1ARNFMD1PRNFM ...... ...... ...... ...... DKAR1F1 DKPR1F1 ... DKAR1FM DKPR1FM...... DKARNF1 DKPRNF1 ... DKARNFM DKPRNFM

FIG. 6A illustrates diamond pattern 600, FIG. 6B illustrates chevronpattern 603, and FIG. 6C illustrates double-peak corrected pattern 604.For double-peak corrected pattern 604, a diamond pattern 600 isperformed on the recorded data and is corrected for double-peak(“ghost”) indications as it was previously described above. Double-peakcorrected pattern 604 may allow an operator to pinpoint the location ofcollar 132 and reduce the spread of collar 132 in the VDL, which maylead to better identification of collars 132 in Wrapped ImageVisualization 300 (Referring to FIG. 3 ).

Wrapped Image Visualization 300 (Referring to FIG. 3 ) disclosed abovemay allow an operator to quickly identify periodic patterns in the dataand easily and quickly pick collars 132 associated with each pipe, evenin cases with a large number of pipes with overlapping collars 132. Acollar removal scheme is described to incrementally remove pipesignatures for the VDL to allow easier identification of collars 132 forsubsequent pipes. The diamond VDL pattern also enhances visual cues thatenable the operator to identify overlapping pipes.

As mentioned above, collar locator algorithms and/or inversion may beutilized by information handling system 144 to determine the location ofcollars 132. This information may then be visualized on an output device152, as described above. Workflow and inversion schemes to determine thelocation of a collar 132 are described below.

A workflow for utilizing a collar locator algorithm is shown in FIG. 7 .The workflow may begin with box 700. Box 702 provides that a cased-holelogging tool (e.g., EM logging tool 100 on FIG. 1 ) may be lowered intoa cased well (e.g., casing string 108 on FIG. 1 ). The cased-holelogging tool may make measurements to obtain a well log and total metalloss (“TML”). The well log may comprise induction measurements performedat least one receiver (e.g., receiver 104 on FIG. 1 ) and at least onefrequency. The excitation may be provided by a transmitter (e.g.,transmitter 102 on FIG. 1 ), placed at a vertical distance from thereceiver (e.g., receiver 104 on FIG. 1 ). The TML measurement may beperformed using a remote field eddy current principles, described above.TML may also be obtained by using external tools that measure only theTML.

Box 704 provides that the well log may be stored in a databaseaccessible through a network, or any other suitable form of a datastorage medium. The well log may be read by an analyst (either over thenetwork or by obtaining the data storage medium) at a post processingcenter (e.g., formation evaluation office). Box 706 provides that theanalyst may import the well log into the inversion software (“IS”). Aschematic description of the IS is shown in FIG. 8 . Box 800 providesIS. Box 802 provides an inversion algorithm (“IA”). Box 804 provides acalibration algorithm (“CA”). Box 806 provides a collar locatoralgorithm (“CLA”). Box 808 provides a weight assignment algorithm(“WAA”). Box 810 provides a ghost detector algorithm (“GDA”). Theunderlying algorithms called (e.g., utilized) by the IS may be explainedin the following steps.

Referring again to FIG. 7 , the IS may load the well plan that belongsto the well that has been logged. Box 708 provides that IS may load thewell plan, make depth adjustments to the well plan based on the welllog. The well plan may show the lengths, start and end depths of allpipes and liners in the completed well. The IS may then compare the wellplan and at least one depth-based curve (e.g., a depth-based measurementsuch as TML) to automatically determine any depth shift that may haveoccurred during logging. This may be done by comparing at least onemajor transition point of the well plan and the depth-based curve.Transition points of the depth-based curve may be the curves where asignificant change happens in the mean amplitude of the signal. Afterfinding the optimal depth shift, the IS may correct all log curves(e.g., depth-based measurements such as receiver voltages, currents,TML, and other depth-based data) for this depth shift.

Box 710 provides that IS may define at least one inversion zone, whichmay be based on TML. Inversion zones may be contiguous, non-overlappinglog sections where the TML may be above a certain severity threshold.This threshold may depend on the needs of the customer. The defaultthreshold may be set at 5% to 20%, for example. In one particularimplementation, the default threshold may be set at 15%.

Box 712 provides that IS may call (e.g., utilize) a CLA to determinecollar locations on at least one concentric pipe. The CLA may takecollar locations on the innermost pipe from a traditional casing collarlocator (“CCL”). The CLA may also determine collar locations on any pipeusing more advanced techniques, such as analyzing the periodic sharpsignatures of collars on a well log. The final output of the CLA may bea binary (i.e., true or false) collar mask array that may indicate thepresence of a collar on any pipe at any depth. The IS may use this maskto optimize the inversion at collar locations (e.g., by allowing morepositive thickness changes in the metal). IS may determine updatedcollar locations on at least one concentric pipe in the wellboreutilizing the collar locator algorithm in the inversion software usingthe well log, well plan and the output log. Additionally, IS maygenerate an updated output log using the updated collar locations, maydetermine updated false metal loss in the output log using the outputlog, well plan and updated collar locations and may generate an updatedoutput log using the false metal loss.

Box 714 provides that the IS may call a WAA that automatically assignsweights to each channel (i.e. receiver/frequency combination) in thecost function associated with the inversion algorithm, as shown in FIG.9 . Box 900 provides well log signals. Box 902 provides a computationalmismatch for the well log signals and the model signals. Box 904provides whether there is a convergence for the computational mismatchof the well log signals and the model signals. Box 906 providesthicknesses of individual pipes. Box 908 provides model signals. Box 910provides updating model parameters. Box 912 provides finding a modelresponse. Box 914 provides calibrating coefficients. Different inversionzones may get different weight assignments, since the number ofconcentric casings may be different in each zone. The weight values maybe determined by previous research on the underlying inversionalgorithm. Two aspects of the inversion algorithm may include: (1) Thesensitivity of each channel to the model parameters (i.e. metalthicknesses on each pipe), (2) possible detrimental factors, such asnoise, model inaccuracy, and measurement inaccuracy. The WAA may assignequal weight to all of the channels.

Referring again to FIG. 7 , box 716 provides that IS may call a CA andmay compute calibration coefficients for a forward model. The CA is runseparately inside each inversion zone. There may be a single calibrationdone for the entire zone, or multiple calibrations inside sub-zones ofsmaller lengths defined by a user of the IS. The CA may statisticallyanalyze a well log in the inversion zone (or sub-zone), and may find anominal zone where the pipes were not corroded and otherwisedefect-free. These zones may be statistically common in a well log,since defects may be an exception, rather than a rule in any given well.The ratios between the measured voltages in a nominal zone and thesimulated voltages from a forward model may be calibration coefficients,which may be applied to a forward model in subsequent inversion runs.

Box 718 provides that IS may call an IA which may estimate thicknessesof individual pipes and may write the estimated thicknesses to an outputlog. The IS may call an IA on each inversion zone. The IA may start withan initial guess for model parameters (i.e., metal thicknesses for eachpipe), and may update these parameters using an optimization algorithm(e.g., Gauss-Newton, Levenberg-Marquardt) until a cost function isminimized. The cost function may be an absolute-square differencebetween a well log and a calibrated forward model result. The IS maydisplay estimated metal thicknesses for each pipe to a user as an outputlog.

Box 720 provides that IS may call a GDA that may determine false metallosses in an output log. The IS may call a GDA that automaticallydetermines ghosts, which are false metal losses that appear as sharp,periodic peaks in the output log. These apparent losses may actually bea consequence of collars; or more specifically, the inability of theinversion algorithm to fully account for their presence due to a finitevertical resolution of the defect EM logging tool 100. Many defectdetection tools have a vertical resolution of several feet, while thelargest collars may have a vertical resolution of about a foot (0.3048meter). The GDA may detect ghosts in an output log automatically in thesame way the CLA detects collar signatures in a well log (i.e., byexploiting a periodicity of ghost signatures). A final output of the GDAmay be a binary ghost mask array that indicates a presence of a ghost(e.g., true or false) on any pipe at any depth.

Box 722 provides that IS may allow a user to re-nm an IA using the ghostinformation from the previous step (e.g., Box 720). The IS may presentto a user (e.g., via a monitor), an option to re-run an inversion (e.g.,starting from Box 718) using the ghost mask array as an inversionconstraint. The inversion constraint may be that the metal losses beassigned zero at locations where the ghost mask is equal to 1, in orderto remove sharp peaks in the output log. For efficiency, the inversionalgorithm may be re-run only at locations where the ghost mask is 1, andthe original results may be kept the same. The IS may present updatedresults to a user. Box 724 provides that Box 710 through Box 720 may berepeated, as necessary. Box 726 provides the end of the workflow.

FIG. 10 illustrates a flow chart that may be used as an example methodto account for coupling properties that includes subtracting theresponses of collars 132 (e.g., shown on FIG. 1 ). At block 1000, themethod may include obtaining response due to collars 132 fromsimulations/measurements. The response due to collars 132 may beobtained for each pipe of a concentric multi-string arrangement. Toobtain coupling responses via simulation, the same pipes configurationmay be simulated while putting collars 132 with known dimensions andelectrical properties on the pipes one at a time to get couplingresponses from each pipe. Alternatively, the collars 132 for all thepipes can be placed in the synthetic model by knowing their positions,dimensions, and properties a priori. These parameters may be also fullyor partially obtained from application of a first round of inversionprocess on the measured data. The coupling responses may also beobtained from measurements. This may be achieved by measuring theresponses of collars 132 of individual pipes separately from knowingtheir positions. Collars 132 responses are then subtracted fromresponses of sections with similar size that include defects as well. Atblock 1002, the method may measure response for each pipe. Themeasurement may include responses of defects and collars 132. At block1004, the method may include subtracting coupling responses fromresponses of each pipe section. This may reduce, and possibly eliminatecoupling responses from the EM log data. At block 1006, the method mayinclude applying inversion to the subtracted responses from block 1004.The inversion may be applied directly to the subtracted responses, forexample, with a modified inversion, for example, including a modifiedcost function that operates based on the differential responses(responses of the defected sections minus responses of the non-defectedsections). Alternatively, applying inversion to the subtracted responsemay include, first, adding the responses at the nominal section(non-defected section) to the subtracted responses from block 1004 andthen applying the inversion, wherein the cost function in the inversionalgorithm does not need to be changed.

FIG. 11 illustrates a flow chart as an example method to account forcoupling properties that includes subtracting the thickness estimationsfor collars 132 (e.g., shown on FIG. 1 ). At block 1100, the method mayinclude obtaining response due to collars 132 fromsimulations/measurements. The response due to collars 132 may beobtained for each pipe of a concentric multi-string arrangement. Theresponse due to collars 132 may be obtained without defects. Asimulation model may be employed to generate responses of receivers 104(e.g., shown on FIG. 1 ) for sections of the pipes that include collars132, but do not include defects. Alternatively, sections of the pipesmay be measured that include collars 132 but do not include defects. Atblock 1102, the method may include applying inversion to obtainthickness variations due to collars 132. The inversion may be applied tothe response due to the pipe couplings from block 1102. At block 1104,the method may measure responses for each pipe that includes defects andcollars 132. At block 1106, inversion may be applied to obtain thicknessvariations due to collars 132 and defects. At block 1108, the method mayinclude subtracting the thickness variations due to collars 132 from thethickness variations due to collars 132 and defects so that thicknessvariations due to defects only may be obtained.

The workflow and inversion schemes discussed above in FIGS. 7-11 may beutilized to determine the location and properties of collar 132. Oncecollar 132 is located, the information may be visualized, as describedabove, by information handling system 144. This visualization may allowfor an operator to easily identify the location of collars 132 in a welllog.

Improvements over other techniques and tools may for example be found invisualization of collars 132 as the disclosed methods and systems offerdiamond shaped collar visualization which may offers a clear view ofcollars 132 to a user. Specifically, this may be beneficial in the caseof overlapping collars 132 and high noise, which may allow a user tovisually identify collars 132 which may not be picked from individualcurves, or other Variable Density Logs and the like. The periodicvisualization may allow the user to track collars 132 of individualpipes on individual casings and identify which pipe collar signaturebelongs to which pipe. This may be beneficial in cases where collarperiodicity (pipe length) may be similar between different pipes andoverlap, which may make it difficult to correctly identify whichsignatures belong to which pipe. The disclosed methods and techniquesmay be beneficial with the increased number of pipes downhole, as moreand more signals overlap making it difficult to separate signalsbelonging to different pipes.

Statement 1: A method for visualizing data to detect a collar maycomprise disposing an electromagnetic logging tool downhole. Theelectromagnetic logging tool may comprise a transmitter and a receiver.The method may further comprise emitting an electromagnetic field fromthe transmitter; energizing a casing with the electromagnetic field toproduce an eddy current; recording the eddy current from the casing withthe receiver; creating a variable-density-log from the recorded eddycurrent; selecting a wrapping period for the variable-density-log;creating a wrapped-variable-density-log from the variable-density-logusing the wrapping period; and determining at least one collar locationand a pipe index with the wrapped-variable-density-log.

Statement 2: The method of statement 1, further comprising adjusting thewrapping period until a substantially horizontal pattern is obtained.

Statement 3: The method of any preceding statement, wherein thedetermining the at least one collar location and the pipe index using asmoothness constraint on a line that connects adjacent identification ofa plurality of collars.

Statement 4: The method of any preceding statement, wherein theadjusting the wrapping period to a second substantially different periodvalue to obtain a second horizontal pattern corresponding to a secondpipe and determining the at least one collar location on the second pipeusing the second horizontal pattern.

Statement 5: The method of any preceding statement, further comprisingproviding visual feedback to a user during the adjusting of the wrappingperiod with a high repetition rate and identifying a horizontal patternfor a plurality of the wrapping periods.

Statement 6: The method of any preceding statement, wherein adjustingthe wrapping period is done automatically using an algorithm thatsearches for an optimum horizontal repetitions.

Statement 7: The method of any preceding statement, further comprisingshowing the at least one collar location on the variable-density-logusing a marker.

Statement 8: The method of any preceding statement, further comprisingshowing the at least one collar location on the variable-density-logusing lines or a curve between a first collar identification and asecond collar identification.

Statement 9: The method of any preceding statement, further comprisingselecting at least one pipe for a collar signature removal; calculatingan ideal signature for the pipes selected for the collar signatureremoval; subtracting the ideal signature for the pipes selected for thecollar signature removal from the existing pick positions in thewrapped-variable-density-log, to obtain a collar removedwrapped-variable-density-log; and using the collar removedwrapped-variable-density-log to determine a second collar location and asecond pipe index.

Statement 10: The method of statement 9, wherein the second pipe indexis different from the pipe index selected for the collar signatureremoval.

Statement 11: The method of statements 9 or 10, further comprisingadjusting an identification position, such that the identificationposition that gives a maximum cancellation of a collar pattern in thecollar removed wrapped-variable-density-log.

Statement 12: The method of statements 9 to 11, further comprisingproviding visual feedback to a user during the adjustment of theidentification position with a high repetition rate and identifying theidentification position that gives a maximum cancellation.

Statement 13: The method of statements 9 to 12, wherein the adjusting anidentification position that gives the maximum cancellation of a collarpattern is chosen as the most accurate position estimate for the collarlocation.

Statement 14: The method of statements 9 to 13, wherein the adjusting anidentification position is performed automatically using an algorithmthat searches for the maximum cancellation of the collar pattern.

Statement 15: The method of statements 9 to 14, wherein the adjusting anidentification position is performed for an overlapping identification.

Statement 16: The method of any preceding claim, wherein the at leastone collar location is plotted in a diamond shape pattern.

Statement 17: The method of any preceding claim, wherein a frequencychannel for a plurality of receivers is placed adjacent to each other.

Statement 18: The method of any preceding claim, wherein a phase and anamplitude are placed on a first half and a second half of thewrapped-variable-density-log.

Statement 19: A system for to detect a collar may comprise: anelectromagnetic logging tool comprising: a transmitter, wherein thetransmitter is a coil; and a receiver, wherein the receiver is a coil;and an information handling system. The information handling system maybe configured to: create a variable-density-log; select a wrappingperiod; create a wrapped-variable-density-log from thevariable-density-log using the wrapping period; and determine at leastone collar location and a pipe index with thewrapped-variable-density-log.

Statement 20, the method of statement 19, wherein the informationhandling machine is further capable to adjust the wrapping period untila substantially horizontal pattern is obtained and determine the atleast one collar location and the pipe index using a smoothnessconstraint on a line that connects adjacent identification of collars.

The preceding description provides various examples of the systems andmethods of use disclosed herein which may contain different method stepsand alternative combinations of components. It should be understoodthat, although individual examples may be discussed herein, the presentdisclosure covers all combinations of the disclosed examples, including,without limitation, the different component combinations, method stepcombinations, and properties of the system. It should be understood thatthe compositions and methods are described in terms of “comprising,”“containing,” or “including” various components or steps, thecompositions and methods can also “consist essentially of” or “consistof” the various components and steps. Moreover, the indefinite articles“a” or “an,” as used in the claims, are defined herein to mean one ormore than one of the element that it introduces.

For the sake of brevity, only certain ranges are explicitly disclosedherein. However, ranges from any lower limit may be combined with anyupper limit to recite a range not explicitly recited, as well as, rangesfrom any lower limit may be combined with any other lower limit torecite a range not explicitly recited, in the same way, ranges from anyupper limit may be combined with any other upper limit to recite a rangenot explicitly recited. Additionally, whenever a numerical range with alower limit and an upper limit is disclosed, any number and any includedrange falling within the range are specifically disclosed. Inparticular, every range of values (of the form, “from about a to aboutb,” or, equivalently, “from approximately a to b,” or, equivalently,“from approximately a-b”) disclosed herein is to be understood to setforth every number and range encompassed within the broader range ofvalues even if not explicitly recited. Thus, every point or individualvalue may serve as its own lower or upper limit combined with any otherpoint or individual value or any other lower or upper limit, to recite arange not explicitly recited.

Therefore, the present examples are well adapted to attain the ends andadvantages mentioned as well as those that are inherent therein. Theparticular examples disclosed above are illustrative only, and may bemodified and practiced in different but equivalent manners apparent tothose skilled in the art having the benefit of the teachings herein.Although individual examples are discussed, the disclosure covers allcombinations of all of the examples. Furthermore, no limitations areintended to the details of construction or design herein shown, otherthan as described in the claims below. Also, the terms in the claimshave their plain, ordinary meaning unless otherwise explicitly andclearly defined by the patentee. It is therefore evident that theparticular illustrative examples disclosed above may be altered ormodified and all such variations are considered within the scope andspirit of those examples. If there is any conflict in the usages of aword or term in this specification and one or more patent(s) or otherdocuments that may be incorporated herein by reference, the definitionsthat are consistent with this specification should be adopted.

What is claimed is:
 1. A method for visualizing data to detect a collarcomprising: disposing an electromagnetic logging tool downhole, whereinthe electromagnetic logging tool comprises: a transmitter; and areceiver; emitting an electromagnetic field from the transmitter;energizing a casing with the electromagnetic field to produce an eddycurrent; recording the eddy current from the casing with the receiver;creating a variable-density-log from the recorded eddy current;selecting a wrapping period for the variable-density-log; creating awrapped-variable-density-log from the variable-density-log using thewrapping period; and determining at least one collar location and a pipeindex with the wrapped-variable-density-log.
 2. The method of claim 1,further comprising: selecting at least one pipe for a collar signatureremoval; calculating an ideal signature for the pipes selected for thecollar signature removal; subtracting the ideal signature for the pipesselected for the collar signature removal from the existing pickpositions in the wrapped-variable-density-log, to obtain a collarremoved wrapped-variable-density-log; and using the collar removedwrapped-variable-density-log to determine a second collar location and asecond pipe index.
 3. The method of claim 2, wherein the second pipeindex is different from the pipe index selected for the collar signatureremoval.
 4. The method of claim 2, further comprising adjusting anidentification position, such that the identification position thatgives a maximum cancellation of a collar pattern in the collar removedwrapped-variable-density-log.
 5. The method of claim 4, furthercomprising providing visual feedback to a user during the adjustment ofthe identification position with a high repetition rate and identifyingthe identification position that gives a maximum cancellation.
 6. Themethod of claim 4, wherein the adjusting an identification position thatgives the maximum cancellation of a collar pattern is chosen as the mostaccurate position estimate for the collar location.
 7. The method ofclaim 4, wherein the adjusting an identification position is performedautomatically using an algorithm that searches for the maximumcancellation of the collar pattern.
 8. The method of claim 4, whereinthe adjusting an identification position is performed for an overlappingidentification.
 9. The method of claim 1, wherein the at least onecollar location is plotted in a diamond shape pattern.
 10. The method ofclaim 9, wherein a frequency channel for a plurality of receivers isplaced adjacent to each other.
 11. The method of claim 9, wherein aphase and an amplitude are placed on a first half and a second half ofthe wrapped-variable-density-log.
 12. A system to detect a collarcomprising: an electromagnetic logging tool comprising: a transmitter,wherein the transmitter is a coil; and a receiver, wherein the receiveris a coil; an information handling system, wherein the informationhandling system is configured to: create a variable-density-log from therecorded eddy current; select a wrapping period for thevariable-density-log; create a wrapped-variable-density-log from thevariable-density-log using the wrapping period; and determine at leastone collar location and a pipe index with thewrapped-variable-density-log.
 13. The system of claim 12, wherein theinformation handling system is further configured to: select at leastone pipe for a collar signature removal; calculate an ideal signaturefor the pipes selected for the collar signature removal; subtract theideal signature for the pipes selected for the collar signature removalfrom the existing pick positions in the wrapped-variable-density-log, toobtain a collar removed wrapped-variable-density-log; and use the collarremoved wrapped-variable-density-log to determine a second collarlocation and a second pipe index.
 14. The system of claim 13, whereinthe second pipe index is different from the pipe index selected for thecollar signature removal.
 15. The system of claim 13, wherein theinformation handling system is further configured to adjust anidentification position, such that the identification position thatgives a maximum cancellation of a collar pattern in the collar removedwrapped-variable-density-log.
 16. The system of claim 15, wherein theinformation handling system is further configured to provide visualfeedback to a user during the adjustment of the identification positionwith a high repetition rate and identifying the identification positionthat gives a maximum cancellation.
 17. The system of claim 15, whereinthe adjusting an identification position that gives the maximumcancellation of a collar pattern is chosen as the most accurate positionestimate for the collar location.
 18. The system of claim 15, whereinthe adjusting an identification position is performed automaticallyusing an algorithm that searches for the maximum cancellation of thecollar pattern.
 19. The system of claim 15, wherein the adjusting anidentification position is performed for an overlapping identification.20. The system of claim 12, wherein the at least one collar location isplotted in a diamond shape pattern, wherein a frequency channel for aplurality of receivers is placed adjacent to each other, and wherein aphase and an amplitude are placed on a first half and a second half ofthe wrapped-variable-density-log.